Econophysics of cryptocurrency crashes: a systematic review

Bielinskyi, Andrii; Serdyuk, Oleksandr; Семеріков, Сергій Олексійович; Соловйов, Володимир Миколайович; Білінський, Андрій Іванович; Сердюк, О. А.

doi:10.31812/123456789/6974

Cited by 5 publications

(3 citation statements)

References 374 publications

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“…Further, for measuring the degree of multifractal cross-correlations between S&P 500, HSI, and BTC, we present the comparative dynamics of the described indicators calculated with the usage of the sliding window approach [5,6] along with the studied series. The presented measures are calculated for the standardized returns of S&P 500, HSI, and BTC, where returns are calculated as…”

Section: Experiments and Empirical Resultsmentioning

confidence: 99%

The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market

Bielinskyi

Соловйов

Solovieva

et al. 2023

Information Technology for Education, Science, and Technics

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In this study, we examine the multifractal cross-correlation relationships between stock and cryptocurrency markets. The measures of complexity which can serve as indicators (indicators-precursors) in both markets are retrieved from Multifractal Detrended Cross-Correlation Analysis. On the example of the S&P 500 and HSI stock indices that are used most by investors to gauge the status of the economy in the world, and the cryptocurrency Bitcoin, which mostly determines the existence of the crypto market, we assess the variation of multifractality and correlations in both markets. Using the sliding window approach, we localize their dynamics across time and indicate a high degree of non-linearity with dominant antipersistency during crash periods for each index. The existence of periods with high and low crosscorrelations for stock and crypto markets provides prospects for reliable trading with several pairs of assets and effective diversification of their risks.

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Section: Experiments and Empirical Resultsmentioning

confidence: 99%

The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market

Bielinskyi

Соловйов

Solovieva

et al. 2023

Information Technology for Education, Science, and Technics

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“…Furthermore, entropy can also help identify unusual patterns or behaviors in the Bitcoin network [ 40 ], providing a possible perspective for preventing cryptocurrency money laundering. As a concept in information theory, entropy [ 41 ] denotes the degree of uncertainty or chaos in a system.…”

Section: Related Workmentioning

confidence: 99%

Bitcoin Money Laundering Detection via Subgraph Contrastive Learning

Ouyang,

Bai,

Feng

et al. 2024

Entropy

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The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

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“…Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. The article "Econophysics of cryptocurrency crashes: a systematic review" [15] by Andrii O. Bielinskyi (figure 4), Oleksandr A. Serdyuk, Serhiy O. Semerikov and Vladimir N. Soloviev review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market.…”

Section: Articles Overviewmentioning

confidence: 99%